How accurate are the longer-term projections of overall survival for cancer immunotherapy for standard versus more flexible parametric extrapolation methods?

Aims To assess the accuracy of standard parametric survival models, spline models, and mixture cure models (MCMs) fitted to overall survival (OS) data available at the time of submission in the NICE HTA process compared with data subsequently made available. Methods Standard parametric distributions, spline models, and MCMs were fitted to OS data presented in single technology appraisals (TAs) for immune-checkpoint inhibitors (ICIs) in cancer. For each TA, the estimated survival from the fitted models was compared with Kaplan-Meier (KM) data that were made available following the HTA submission using differences between point estimates and restricted area under the curve (AUC) at both the midpoint and the end of additional follow-up. Differences in interval AUC values (calculated for each 6-month period) were also assessed. Results Standard parametric survival models and spline models were more likely to underestimate longer-term survival, irrespective of the measure used to assess model accuracy. MCMs were more likely to overestimate survival; however, this was improved in some cases by applying an additional hazard of mortality for "statistically cured" patients. Limitations The accuracy of the models was assessed based on much shorter OS data than the period for which extrapolation is needed, which may impact conclusions regarding the most accurate models. The most recent TAs for ICIs have not been captured. Conclusions There are no definitive findings that unquestionably support the use of one specific extrapolation technique. Rather, each has the potential to provide accurate or inaccurate extrapolation to longer-term data in certain circumstances, but the added flexibility of more complex models can be justified for treatments, like ICIs, that have extended survival for patients across disease areas. The use of mortality adjustments for "statistically cured" patients allows decision-makers to explore more conservative scenarios in the face of high decision uncertainty.